🧠 CNN - Convolutional Neural Network

# CNN Gauge Reading
import tensorflow as tf
import cv2
import numpy as np

# Load trained model
model = tf.keras.models.load_model('gauge_cnn_model.h5')

# Preprocess gauge ROI from YOLO
def preprocess_gauge(roi_image):
    # Resize to model input size
    resized = cv2.resize(roi_image, (224, 224))
    
    # Normalize pixel values
    normalized = resized / 255.0
    
    # Add batch dimension
    batch_input = np.expand_dims(normalized, axis=0)
    
    return batch_input

# Predict gauge value
def read_gauge_value(roi_image, gauge_type):
    # Preprocess image
    processed_img = preprocess_gauge(roi_image)
    
    # Run inference
    predictions = model.predict(processed_img)
    
    # Post-process based on gauge type
    if gauge_type == 'analog':
        # Convert angle to value
        angle = predictions[0] * 360
        value = angle_to_value(angle, gauge_config)
    elif gauge_type == 'digital':
        # Direct digit recognition
        value = predictions[0]
    
    return value, confidence_score

# Example usage
gauge_roi = cv2.imread('gauge_crop.jpg')
value, confidence = read_gauge_value(gauge_roi, 'analog')
print(f"Gauge Reading: {value:.2f}, Confidence: {confidence:.3f}")